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Complexity traits and dynamics of tourism destinations

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  • Sainaghi, Ruggero
  • Baggio, Rodolfo

Abstract

This paper is rooted in network science and contributes to filling two gaps, developing multiple case studies in order i) to measure the complex structure of tourism destination and ii) to explore its evolution over time, by mapping turning points. The findings put forward ten new analyses, allowing the research team to test two hypotheses: i) concerning the complex structure, the tendency of tourism destinations to remain far from the chaos threshold, ii) concerning turning points, the ability of different destinations to show also different evolution through time. The paper uses the Horizontal Visibility Graph Algorithm and applies it to a sample of ten tourism destinations in the second leading Italian tourism region per size: Trentino-Alto Adige. Findings confirm both hypotheses. Limitations and research implications are drawn.

Suggested Citation

  • Sainaghi, Ruggero & Baggio, Rodolfo, 2017. "Complexity traits and dynamics of tourism destinations," Tourism Management, Elsevier, vol. 63(C), pages 368-382.
    • Handle: RePEc:eee:touman:v:63:y:2017:i:c:p:368-382
    DOI: 10.1016/j.tourman.2017.07.004
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    Cited by:

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    3. Chien-Chiang Lee & Mei-Ping Chen & Yi-Ting Peng, 2021. "Tourism development and happiness: International evidence," Tourism Economics, , vol. 27(5), pages 1101-1136, August.
    4. Hwayoon Seok & George A. Barnett & Yoonjae Nam, 2021. "A social network analysis of international tourism flow," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 419-439, April.
    5. Silvia Emili & Federica Galli, 2023. "Spatial and cross-sectoral input spillover effects: the case of the Italian tourism industry," Journal of Productivity Analysis, Springer, vol. 59(3), pages 243-258, June.
    6. César Daniel Aguilar-Becerra & Oscar Frausto-Martínez & Hernando Avilés-Pineda & Jair J. Pineda-Pineda & Jennifer Caroline Soares & Maximino Reyes Umaña, 2019. "Path Dependence and Social Network Analysis on Evolutionary Dynamics of Tourism in Coastal Rural Communities," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
    7. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    8. Ki-Hong Choi & Insin Kim, 2021. "Co-Movement between Tourist Arrivals of Inbound Tourism Markets in South Korea: Applying the Dynamic Copula Method Using Secondary Time Series Data," Sustainability, MDPI, vol. 13(3), pages 1-13, January.

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